Handling multi-rate and missing data in system identification

In the present work we consider the problem of subspace-based system identification of batch processes subject to multi-rate and missing data. To this end, we develop a state-space system identification approach for batch processes capable of handling multi-rate and missing data by utilizing the incremental singular value decomposition technique. Simulation case studies involving application to the electric arc furnace process demonstrate the efficacy of the proposed modeling approach compared to traditional identification subject to limited availability of process measurements, missing data and measurement noise.